Data & Analytics Engineer

Overview

Data & Analytics Engineer to own dashboard delivery end to end and the AWS pipelines behind them.

What you'll do

• Own dashboard changes end to end in QuickSight and ThoughtSpot, including new metrics/filters and SPICE refresh management.
• Build and maintain batch and streaming ETL pipelines on AWS using Glue (PySpark), S3, Redshift, and Airflow (MWAA) DAGs.
• Write and optimize Redshift SQL, and debug query performance, connection contention, and data mismatches.
• Support near-real-time ingestion using Kafka/MSK CDC through Glue Streaming into S3 and Redshift.
• Investigate analytics discrepancies reported via Jira/support tickets, root-cause issues in data, and communicate findings.
• Set up and respond to pipeline monitoring using CloudWatch metrics/alarms and monitoring DAGs, including incident triage and RCA.
• Ensure data quality, validation, and consistency across systems.

What you'll need

• 3-4 years of experience in data engineering and/or analytics engineering.
• Strong SQL on Redshift (or a similar MPP warehouse).
• Solid Python/PySpark skills.
• Hands-on AWS experience with S3, Glue, Redshift, and CloudWatch.
• Workflow orchestration experience with Apache Airflow (authoring, debugging, and deploying DAGs).
• BI dashboarding experience with QuickSight, ThoughtSpot, Tableau, or Power BI.
• Comfort coordinating across product, engineering, DevOps, and customer-facing teams.

Nice to have

• Streaming/CDC experience with Kafka (MSK), Debezium, or Spark Structured Streaming.
• AWS infrastructure awareness including IAM roles, security groups, Secrets Manager, and Kinesis/Firehose.
• Fintech or B2B SaaS analytics exposure.